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Institute
- Theodor-Boveri-Institut für Biowissenschaften (83)
- Fakultät für Physik und Astronomie (38)
- Graduate School of Life Sciences (32)
- Institut für Psychologie (25)
- Neurologische Klinik und Poliklinik (24)
- Institut für Molekulare Infektionsbiologie (23)
- Klinik und Poliklinik für Psychiatrie, Psychosomatik und Psychotherapie (21)
- Physikalisches Institut (19)
- Rudolf-Virchow-Zentrum (16)
- Lehrstuhl für Biochemie (15)
Sonstige beteiligte Institutionen
Background: The Global initiative for chronic Obstructive Lung Disease (GOLD) defines COPD as a fixed postbronchodilator ratio of forced expiratory volume in 1 second and forced vital capacity (FEV1/FVC) below 0.7. Agedependent cut-off values below the lower fifth percentile (LLN) of this ratio derived from the general population have been proposed as an alternative. We wanted to assess the diagnostic accuracy and prognostic capability of the GOLD and LLN definition when compared to an expert-based diagnosis. Methods: In a prospective cohort study, 405 patients aged ≥ 65 years with a general practitioner’s diagnosis of COPD were recruited and followed up for 4.5 (median; quartiles 3.9; 5.1) years. Prevalence rates of COPD according to GOLD and three LLN definitions and diagnostic performance measurements were calculated. The reference standard was the diagnosis of COPD of an expert panel that used all available diagnostic information, including spirometry and bodyplethysmography. Results: Compared to the expert panel diagnosis, ‘GOLD-COPD’ misclassified 69 (28%) patients, and the three LLNs misclassified 114 (46%), 96 (39%), and 98 (40%) patients, respectively. The GOLD classification led to more false positives, the LLNs to more false negative diagnoses. The main predictors beyond the FEV1/FVC ratio for an expert diagnosis of COPD were the FEV1 % predicted, and the residual volume/total lung capacity ratio (RV/TLC). Adding FEV1 and RV/TLC to GOLD or LLN improved the diagnostic accuracy, resulting in a significant reduction of up to 50% of the number of misdiagnoses. The expert diagnosis of COPD better predicts exacerbations, hospitalizations and mortality than GOLD or LLN. Conclusions: GOLD criteria over-diagnose COPD, while LLN definitions under-diagnose COPD in elderly patients as compared to an expert panel diagnosis. Incorporating FEV1 and RV/TLC into the GOLD-COPD or LLN-based definition brings both definitions closer to expert panel diagnosis of COPD, and to daily clinical practice.
1,25-dihydroxyvitamin D3 (1,25D3) was reported to induce premature organismal aging in fibroblast growth factor-23 (Fgf23) and klotho deficient mice, which is of main interest as 1,25D3 supplementation of its precursor cholecalciferol is used in basic osteoporosis treatment. We wanted to know if 1,25D3 is able to modulate aging processes on a cellular level in human mesenchymal stem cells (hMSC). Effects of 100 nM 1,25D3 on hMSC were analyzed by cell proliferation and apoptosis assay, beta-galactosidase staining, VDR and surface marker immunocytochemistry, RT-PCR of 1,25D3-responsive, quiescence-and replicative senescence-associated genes. 1,25D3 treatment significantly inhibited hMSC proliferation and apoptosis after 72 h and delayed the development of replicative senescence in long-term cultures according to beta-galactosidase staining and P16 expression. Cell morphology changed from a fibroblast like appearance to broad and rounded shapes. Long term treatment did not induce lineage commitment in terms of osteogenic pathways but maintained their clonogenic capacity, their surface marker characteristics (expression of CD73, CD90, CD105) and their multipotency to develop towards the chondrogenic, adipogenic and osteogenic pathways. In conclusion, 1,25D3 delays replicative senescence in primary hMSC while the pro-aging effects seen in mouse models might mainly be due to elevated systemic phosphate levels, which propagate organismal aging.
Background: Ga-[1,4,7,10-tetraazacyclododecane-N,N0,N00,N000-tetraacetic acid]-d-Phe1,Tyr3-octreotate (DOTATATE) positron emission tomography (PET) is commonly used for the visualization of somatostatin receptor (SSTR)-positive neuroendocrine tumors. SSTR is also known to be expressed on macrophages, which play a major role in inflammatory processes in the walls of coronary arteries and large vessels. Therefore, imaging SSTR expression has the potential to visualize vulnerable plaques. We assessed 68Ga-DOTATATE accumulation in large vessels in comparison to 18F-2-fluorodeoxyglucose (FDG) uptake, calcified plaques (CPs), and cardiovascular risk factors. Methods: Sixteen consecutive patients with neuroendocrine tumors or thyroid cancer underwent both 68Ga-DOTATATE and 18F-FDG PET/CT for staging or restaging purposes. Detailed clinical data, including common cardiovascular risk factors, were recorded. For a separate assessment, they were divided into a high-risk and a low-risk group. In each patient, we calculated the maximum target-to-background ratio (TBR) of eight arterial segments. The correlation of the TBRmean of both tracers with risk factors including plaque burden was assessed. Results: The mean TBR of 68Ga-DOTATATE in all large arteries correlated significantly with the presence of CPs (r = 0.52; p < 0.05), hypertension (r = 0.60; p < 0.05), age (r = 0.56; p < 0.05), and uptake of 18F-FDG (r = 0.64; p < 0.01). There was one significant correlation between 18F-FDG uptake and hypertension (0.58; p < 0.05). Out of the 37 sites with the highest focal 68Ga-DOTATATE uptake, 16 (43.2%) also had focal 18F-FDG uptake. Of 39 sites with the highest 18F-FDG uptake, only 11 (28.2%) had a colocalized 68Ga-DOTATATE accumulation. Conclusions: In this series of cancer patients, we found a stronger association of increased 68Ga-DOTATATE uptake with known risk factors of cardiovascular disease as compared to 18F-FDG, suggesting a potential role for plaque imaging in large arteries. Strikingly, we found that focal uptake of 68Ga-DOTATATE and 18F-FDG does not colocalize in a significant number of lesions.
Age is one of the most salient aspects in faces and of fundamental cognitive and social relevance. Although face processing has been studied extensively, brain regions responsive to age have yet to be localized. Using evocative face morphs and fMRI, we segregate two areas extending beyond the previously established face-sensitive core network, centered on the inferior temporal sulci and angular gyri bilaterally, both of which process changes of facial age. By means of probabilistic tractography, we compare their patterns of functional activation and structural connectivity. The ventral portion of Wernicke’s understudied perpendicular association fasciculus is shown to interconnect the two areas, and activation within these clusters is related to the probability of fiber connectivity between them. In addition, post-hoc age-rating competence is found to be associated with high response magnitudes in the left angular gyrus. Our results provide the first evidence that facial age has a distinct representation pattern in the posterior human brain. We propose that particular face-sensitive nodes interact with additional object-unselective quantification modules to obtain individual estimates of facial age. This brain network processing the age of faces differs from the cortical areas that have previously been linked to less developmental but instantly changeable face aspects. Our probabilistic method of associating activations with connectivity patterns reveals an exemplary link that can be used to further study, assess and quantify structure-function relationships.
The superconducting properties of complex materials like the recently discovered iron-pnictides or strontium-ruthenate are often governed by multi-orbital effects. In order to unravel the superconductivity of those materials, we develop a multi-orbital implementation of the functional renormalization group and study the pairing states of several characteristic material systems. Starting with the iron-pnictides, we find competing spin-fluctuation channels that become attractive if the superconducting gap changes sign between the nested portions of the Fermi surface. Depending on material details like doping or pnictogen height, these spin fluctuations then give rise to $s_{\pm}$-wave pairing with or without gap nodes and, in some cases, also change the symmetry to $d$-wave. Near the transition from nodal $s_{\pm}$-wave to $d$-wave pairing, we predict the occurrence of a time-reversal symmetry-broken $(s+id)$-pairing state which avoids gap nodes and is therefore energetically favored. We further study the electronic instabilities of doped graphene, another fascinating material which has recently become accessible and which can effectively be regarded as multi-orbital system. Here, the hexagonal lattice structure assures the degeneracy of two $d$-wave pairing channels, and the system then realizes a chiral $(d+id)$-pairing state in a wide doping range around van-Hove filling. In addition, we also find spin-triplet pairing as well as an exotic spin-density wave phase which both become leading if the long-ranged hopping or interaction parameters are slightly modified, for example, by choosing different substrate materials. Finally, we consider the superconducting state of strontium-ruthenate, a possible candidate for chiral spin-triplet pairing with fascinating properties like the existence of half-quantum vortices obeying non-Abelian statistics. Using a microscopic three orbital description including spin-orbit coupling, we demonstrate that ferromagnetic fluctuations are still sufficient to induce this $\bs{\hat{z}}(p_x\pm ip_y)$-pairing state. The resulting superconducting gap reveals strong anisotropies on the $d_{xy}$-dominated Fermi-surface pocket and nearly vanishes on the other remaining two pockets.
Glioblastomas are characterized by an aggressive local growth pattern, a marked degree of invasiveness and poor prognosis. Tumor invasiveness is facilitated by the increased activity of proteolytic enzymes which are involved in destruction of the extracellular matrix of the surrounding healthy brain tissue. Elevated levels of matrix metalloproteinases (MMPs) were found in glioblastoma (GBM) cell-lines, as well as in GBM biopsies as compared with low-grade astrocytoma (LGA) and normal brain samples, indicating a role in malignant progression. A careful review of the available literature revealed that both the expression and role of several of the 23 human MMP proteins is controversely discussed and for some there are no data available at all. We therefore screened a panel of 15 LGA and 15 GBM biopsy samples for those MMPs for which there is either no, very limited or even contradictory data available. Hence, this is the first complete compilation of the expression pattern of all 23 human MMPs in astrocytic tumors. This study will support a better understanding of the specific expression patterns and interaction of proteolytic enzymes in malignant human glioma and may provide additional starting points for targeted patient therapy.
A remarkable feature of many small non-coding RNAs (sRNAs) of Escherichia coli and Salmonella is their accumulation in the stationary phase of bacterial growth. Several stress response regulators and sigma factors have been reported to direct the transcription of stationary phase-specific sRNAs, but a widely conserved sRNA gene that is controlled by the major stationary phase and stress sigma factor, Sigma(S) (RpoS), has remained elusive. We have studied in Salmonella the conserved SdsR sRNA, previously known as RyeB, one of the most abundant stationary phase-specific sRNAs in E. coli. Alignments of the sdsR promoter region and genetic analysis strongly suggest that this sRNA gene is selectively transcribed by Sigma(S). We show that SdsR down-regulates the synthesis of the major Salmonella porin OmpD by Hfq-dependent base pairing; SdsR thus represents the fourth sRNA to regulate this major outer membrane porin. Similar to the InvR, MicC and RybB sRNAs, SdsR recognizes the ompD mRNA in the coding sequence, suggesting that this mRNA may be primarily targeted downstream of the start codon. The SdsR-binding site in ompD was localized by 3'-RACE, an experimental approach that promises to be of use in predicting other sRNA-target interactions in bacteria.
In previous studies of a genetic isolate, we identified significant linkage of attention deficit hyperactivity disorder (ADHD) to 4q, 5q, 8q, 11q and 17p. The existence of unique large size families linked to multiple regions, and the fact that these families came from an isolated population, we hypothesized that two-locus interaction contributions to ADHD were plausible. Several analytical models converged to show significant interaction between 4q and 11q (P<1 × 10−8) and 11q and 17p (P<1 × 10−6). As we have identified that common variants of the LPHN3 gene were responsible for the 4q linkage signal, we focused on 4q–11q interaction to determine that single-nucleotide polymorphisms (SNPs) harbored in the LPHN3 gene interact with SNPs spanning the 11q region that contains DRD2 and NCAM1 genes, to double the risk of developing ADHD. This interaction not only explains genetic effects much better than taking each of these loci effects by separated but also differences in brain metabolism as depicted by proton magnetic resonance spectroscopy data and pharmacogenetic response to stimulant medication. These findings not only add information about how high order genetic interactions might be implicated in conferring susceptibility to develop ADHD but also show that future studies of the effects of genetic interactions on ADHD clinical information will help to shape predictive models of individual outcome.
Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.